"""A Gradio app for anonymizing text data using FHE.""" import base64 import os import re import subprocess import time import uuid from typing import Dict, List import gradio as gr import numpy import pandas as pd import requests from fhe_anonymizer import FHEAnonymizer from openai import OpenAI from utils_demo import * from concrete.ml.deployment import FHEModelClient # Ensure the directory is clean before starting processes or reading files clean_directory() anonymizer = FHEAnonymizer() client = OpenAI(api_key=os.environ.get("openaikey")) # Start the Uvicorn server hosting the FastAPI app subprocess.Popen(["uvicorn", "server:app"], cwd=CURRENT_DIR) time.sleep(3) # Load data from files required for the application UUID_MAP = read_json(MAPPING_UUID_PATH) ANONYMIZED_DOCUMENT = read_txt(ANONYMIZED_FILE_PATH) MAPPING_ANONYMIZED_SENTENCES = read_pickle(MAPPING_ANONYMIZED_SENTENCES_PATH) MAPPING_ENCRYPTED_SENTENCES = read_pickle(MAPPING_ENCRYPTED_SENTENCES_PATH) ORIGINAL_DOCUMENT = read_txt(ORIGINAL_FILE_PATH).split("\n\n") print(ORIGINAL_DOCUMENT) # 4. Data Processing and Operations (No specific operations shown here, assuming it's part of anonymizer or client usage) # 5. Utilizing External Services or APIs # (Assuming client initialization and anonymizer setup are parts of using external services or application-specific logic) # Generate a random user ID for this session USER_ID = numpy.random.randint(0, 2**32) def select_static_anonymized_sentences_fn(selected_sentences: List): selected_sentences = [MAPPING_ANONYMIZED_SENTENCES[sentence] for sentence in selected_sentences] anonymized_selected_sentence = sorted(selected_sentences, key=lambda x: x[0]) anonymized_selected_sentence = [sentence for _, sentence in anonymized_selected_sentence] return {anonymized_doc_box: gr.update(value="\n\n".join(anonymized_selected_sentence))} def key_gen_fn() -> Dict: """Generate keys for a given user.""" print("------------ Step 1: Key Generation:") print(f"Your user ID is: {USER_ID}....") client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{USER_ID}") client.load() # Creates the private and evaluation keys on the client side client.generate_private_and_evaluation_keys() # Get the serialized evaluation keys serialized_evaluation_keys = client.get_serialized_evaluation_keys() assert isinstance(serialized_evaluation_keys, bytes) # Save the evaluation key evaluation_key_path = KEYS_DIR / f"{USER_ID}/evaluation_key" write_bytes(evaluation_key_path, serialized_evaluation_keys) # anonymizer.generate_key() if not evaluation_key_path.is_file(): error_message = ( f"Error Encountered While generating the evaluation {evaluation_key_path.is_file()=}" ) print(error_message) return {gen_key_btn: gr.update(value=error_message)} else: print("Keys have been generated ✅") return {gen_key_btn: gr.update(value="Keys have been generated ✅")} def select_static_encrypted_sentences_fn(selected_sentences: List): selected_sentences = [MAPPING_ENCRYPTED_SENTENCES[sentence] for sentence in selected_sentences] anonymized_selected_sentence = sorted(selected_sentences, key=lambda x: x[0]) anonymized_selected_sentence = [sentence for _, sentence in anonymized_selected_sentence] return {encrypted_doc_box: gr.update(value="\n\n".join(anonymized_selected_sentence))} def encrypt_query_fn(query): print(f"\n------------ Step 2: Query encryption: {query=}") if not (KEYS_DIR / f"{USER_ID}/evaluation_key").is_file(): return {output_encrypted_box: gr.update(value="Error ❌: Please generate the key first!")} if is_user_query_valid(query): return { query_box: gr.update( value=( "Unable to process ❌: The request exceeds the length limit or falls " "outside the scope of this document. Please refine your query." ) ) } # Retrieve the client API client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{USER_ID}") client.load() encrypted_tokens = [] # Pattern to identify words and non-words (including punctuation, spaces, etc.) tokens = re.findall(r"(\b[\w\.\/\-@]+\b|[\s,.!?;:'\"-]+)", query) for token in tokens: # 1- Ignore non-words tokens if bool(re.match(r"^\s+$", token)): continue # 2- Directly append non-word tokens or whitespace to processed_tokens # Prediction for each word emb_x = get_batch_text_representation([token], EMBEDDINGS_MODEL, TOKENIZER) encrypted_x = client.quantize_encrypt_serialize(emb_x) assert isinstance(encrypted_x, bytes) encrypted_tokens.append(encrypted_x) print("Data encrypted ✅ on Client Side") assert len({len(token) for token in encrypted_tokens}) == 1 write_bytes(KEYS_DIR / f"{USER_ID}/encrypted_input", b"".join(encrypted_tokens)) write_bytes( KEYS_DIR / f"{USER_ID}/encrypted_input_len", len(encrypted_tokens[0]).to_bytes(10, "big") ) encrypted_quant_tokens_hex = [token.hex()[500:580] for token in encrypted_tokens] return { output_encrypted_box: gr.update(value=" ".join(encrypted_quant_tokens_hex), lines=4), anonymized_text_output: gr.update(visible=True, value=None), identified_words_output_df: gr.update(visible=False, value=None), } def send_input_fn(query) -> Dict: """Send the encrypted data and the evaluation key to the server.""" print("------------ Step 3.1: Send encrypted_data to the Server") evaluation_key_path = KEYS_DIR / f"{USER_ID}/evaluation_key" encrypted_input_path = KEYS_DIR / f"{USER_ID}/encrypted_input" encrypted_input_len_path = KEYS_DIR / f"{USER_ID}/encrypted_input_len" if not evaluation_key_path.is_file(): error_message = ( "Error Encountered While Sending Data to the Server: " f"The key has been generated correctly - {evaluation_key_path.is_file()=}" ) return {anonymized_text_output: gr.update(value=error_message)} if not encrypted_input_path.is_file(): error_message = ( "Error Encountered While Sending Data to the Server: The data has not been encrypted " f"correctly on the client side - {encrypted_input_path.is_file()=}" ) return {anonymized_text_output: gr.update(value=error_message)} # Define the data and files to post data = {"user_id": USER_ID, "input": query} files = [ ("files", open(evaluation_key_path, "rb")), ("files", open(encrypted_input_path, "rb")), ("files", open(encrypted_input_len_path, "rb")), ] # Send the encrypted input and evaluation key to the server url = SERVER_URL + "send_input" with requests.post( url=url, data=data, files=files, ) as resp: print("Data sent to the server ✅" if resp.ok else "Error ❌ in sending data to the server") def run_fhe_in_server_fn() -> Dict: """Run in FHE the anonymization of the query""" print("------------ Step 3.2: Run in FHE on the Server Side") evaluation_key_path = KEYS_DIR / f"{USER_ID}/evaluation_key" encrypted_input_path = KEYS_DIR / f"{USER_ID}/encrypted_input" if not evaluation_key_path.is_file(): error_message = ( "Error Encountered While Sending Data to the Server: " f"The key has been generated correctly - {evaluation_key_path.is_file()=}" ) return {anonymized_text_output: gr.update(value=error_message)} if not encrypted_input_path.is_file(): error_message = ( "Error Encountered While Sending Data to the Server: The data has not been encrypted " f"correctly on the client side - {encrypted_input_path.is_file()=}" ) return {anonymized_text_output: gr.update(value=error_message)} data = { "user_id": USER_ID, } url = SERVER_URL + "run_fhe" with requests.post( url=url, data=data, ) as response: if not response.ok: return { anonymized_text_output: gr.update( value=( "⚠️ An error occurred on the Server Side. " "Please check connectivity and data transmission." ), ), } else: time.sleep(1) print(f"The query anonymization was computed in {response.json():.2f} s per token.") def get_output_fn() -> Dict: print("------------ Step 3.3: Get the output from the Server Side") if not (KEYS_DIR / f"{USER_ID}/evaluation_key").is_file(): error_message = ( "Error Encountered While Sending Data to the Server: " "The key has not been generated correctly" ) return {anonymized_text_output: gr.update(value=error_message)} if not (KEYS_DIR / f"{USER_ID}/encrypted_input").is_file(): error_message = ( "Error Encountered While Sending Data to the Server: " "The data has not been encrypted correctly on the client side" ) return {anonymized_text_output: gr.update(value=error_message)} data = { "user_id": USER_ID, } # Retrieve the encrypted output url = SERVER_URL + "get_output" with requests.post( url=url, data=data, ) as response: if response.ok: print("Data received ✅ from the remote Server") response_data = response.json() encrypted_output_base64 = response_data["encrypted_output"] length_encrypted_output_base64 = response_data["length"] # Decode the base64 encoded data encrypted_output = base64.b64decode(encrypted_output_base64) length_encrypted_output = base64.b64decode(length_encrypted_output_base64) # Save the encrypted output to bytes in a file as it is too large to pass through # regular Gradio buttons (see https://github.com/gradio-app/gradio/issues/1877) write_bytes(CLIENT_DIR / f"{USER_ID}_encrypted_output", encrypted_output) write_bytes(CLIENT_DIR / f"{USER_ID}_encrypted_output_len", length_encrypted_output) else: print("Error ❌ in getting data to the server") def decrypt_fn(text) -> Dict: """Dencrypt the data on the `Client Side`.""" print("------------ Step 4: Dencrypt the data on the `Client Side`") # Get the encrypted output path encrypted_output_path = CLIENT_DIR / f"{USER_ID}_encrypted_output" if not encrypted_output_path.is_file(): error_message = """⚠️ Please ensure that: \n - the connectivity \n - the query has been submitted \n - the evaluation key has been generated \n - the server processed the encrypted data \n - the Client received the data from the Server before decrypting the prediction """ print(error_message) return error_message, None # Retrieve the client API client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{USER_ID}") client.load() # Load the encrypted output as bytes encrypted_output = read_bytes(CLIENT_DIR / f"{USER_ID}_encrypted_output") length = int.from_bytes(read_bytes(CLIENT_DIR / f"{USER_ID}_encrypted_output_len"), "big") tokens = re.findall(r"(\b[\w\.\/\-@]+\b|[\s,.!?;:'\"-]+)", text) decrypted_output, identified_words_with_prob = [], [] i = 0 for token in tokens: # Directly append non-word tokens or whitespace to processed_tokens if bool(re.match(r"^\s+$", token)): continue else: encrypted_token = encrypted_output[i : i + length] prediction_proba = client.deserialize_decrypt_dequantize(encrypted_token) probability = prediction_proba[0][1] i += length if probability >= 0.77: identified_words_with_prob.append((token, probability)) # Use the existing UUID if available, otherwise generate a new one tmp_uuid = UUID_MAP.get(token, str(uuid.uuid4())[:8]) decrypted_output.append(tmp_uuid) UUID_MAP[token] = tmp_uuid else: decrypted_output.append(token) # Update the UUID map with query. write_json(MAPPING_UUID_PATH, UUID_MAP) # Removing Spaces Before Punctuation: anonymized_text = re.sub(r"\s([,.!?;:])", r"\1", " ".join(decrypted_output)) # Convert the list of identified words and probabilities into a DataFrame if identified_words_with_prob: identified_df = pd.DataFrame( identified_words_with_prob, columns=["Identified Words", "Probability"] ) else: identified_df = pd.DataFrame(columns=["Identified Words", "Probability"]) print("Decryption done ✅ on Client Side") return anonymized_text, identified_df def anonymization_with_fn(query): encrypt_query_fn(query) send_input_fn(query) run_fhe_in_server_fn() get_output_fn() anonymized_text, identified_df = decrypt_fn(query) return { anonymized_text_output: gr.update(value=anonymized_text), identified_words_output_df: gr.update(value=identified_df, visible=True), } def query_chatgpt_fn(anonymized_query, anonymized_document): print("------------ Step 5: ChatGPT communication") if not (KEYS_DIR / f"{USER_ID}/evaluation_key").is_file(): error_message = "Error ❌: Please generate the key first!" return {chatgpt_response_anonymized: gr.update(value=error_message)} if not (CLIENT_DIR / f"{USER_ID}_encrypted_output").is_file(): error_message = "Error ❌: Please encrypt your query first!" return {chatgpt_response_anonymized: gr.update(value=error_message)} prompt = read_txt(PROMPT_PATH) # Prepare prompt initial_prompt = prompt + "\n" query = ( "Document content:\n```\n" + anonymized_document + "\n\n```" + "Query:\n```\n" + anonymized_query + "\n```" ) print(f'initial_prompt:\n{initial_prompt}') completion = client.chat.completions.create( model="gpt-4-1106-preview", # Replace with "gpt-4" if available messages=[ {"role": "system", "content": prompt}, {"role": "user", "content": query}, ], ) anonymized_response = completion.choices[0].message.content uuid_map = read_json(MAPPING_UUID_PATH) inverse_uuid_map = { v: k for k, v in uuid_map.items() } # TODO load the inverse mapping from disk for efficiency # Pattern to identify words and non-words (including punctuation, spaces, etc.) tokens = re.findall(r"(\b[\w\.\/\-@]+\b|[\s,.!?;:'\"-]+)", anonymized_response) processed_tokens = [] for token in tokens: # Directly append non-word tokens or whitespace to processed_tokens if not token.strip() or not re.match(r"\w+", token): processed_tokens.append(token) continue if token in inverse_uuid_map: processed_tokens.append(inverse_uuid_map[token]) else: processed_tokens.append(token) deanonymized_response = "".join(processed_tokens) return {chatgpt_response_anonymized: gr.update(value=anonymized_response), chatgpt_response_deanonymized: gr.update(value=deanonymized_response)} demo = gr.Blocks(css=".markdown-body { font-size: 18px; }") with demo: gr.Markdown( """
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